Here’s a rough guide to spotting junk science articles on the Internet

Sadly, a majority of people who share information online rarely care to look into the sources and research of the information they’re sharing.

The skill of sharing reliable info is something that’s hard to teach to a quackery-drenched public, but the chemistry-based website compoundchem.com gave it a shot. In a graphic called A Rough Guide to Spotting Bad Science, 12 steps are given as a practice to help ward off the sharing of junk articles on the Internet.

“Being able to evaluate the evidence behind a scientific claim is important. Being able to [recognize] bad science reporting, or faults in scientific studies, is equally important. These 12 points will help you separate the science from the pseudoscience,” the graphic’s intro reads.

Since the text on certain parts of the graphic may be hard to read, here’s its 12 points below:

Sensationalized Headlines

Article headlines are commonly designed to entice viewers into clicking on and reading the article. At times, they can oversimplify the findings of scientific research. At worst, they sensationalize and misrepresent them.

Misinterpreted Results

News articles can distort or misinterpret the findings of research for the sake of a good story, whether intentionally or otherwise. If possible, try to read the original research rather than relying on the article based on it for information.

Conflicts of Interest

Many companies will employ scientists to carry out and publish research – whilst this doesn’t necessarily invalidate research, it should be analyzed with this in mind. Research can also be misinterpreted for personal or financial gain.

Correlation & Causation

Be wary of any confusion of correlation and causation. A correlation between variables doesn’t always mean one causes the other. Global warming increased since the 1800s, and pirate numbers decreased, but a lack of pirates doesn’t cause global warming.

Unsupported Conclusions

Speculation can often help to drive science forward. However, studies should be clear on the fact their study proves, and which conclusions are as yet unsupported ones. A statement framed by speculative language may require further evidence to confirm.

Problems With Sample Size

In trials, the smaller a sample size, the lower the confidence in the results from that sample. Conclusions drawn can still be valid, and in some cases small samples are unavoidable, but larger samples often give more representative results.

Unrepresentative Samples Used

In human trials, subjects are selected that are representative of a larger population. If the sample is different from the population as a whole, then the conclusions of the trial may be biased towards a particular outcome.

No Control Group Used

In clinical trials, results from test subjects should be compared to a ‘control group’ not given the substance being tested. Groups should also be allocated randomly. In general experiments, a control test should be used where all variables are controlled.

No Blind Testing Used

To try and prevent bias, subjects should not know if they are in the test or the control group. In ‘double blind’ testing, even researchers don’t know which group subjects are in after testing. Note: blind testing isn’t always feasible, or ethical.

Selective Reporting of Data

Also known as ‘cherry picking,’ this involves selecting data from results which supports the conclusion of the research, whilst ignoring those that do not. If a research paper draws conclusions from a selection of its results and not all, it may be guilty of this.

Unreplicable Results

Results should be replicable by independent research and tested over a wide range of conditions (where possible) to ensure they are consistent. Extraordinary claims require extraordinary evidence – that is, much more than one independent study!

Non-Peer-Reviewed Material

Peer review is an important part of the scientific process. Other scientists appraise and critique studies before publication in a journal. Research that has not gone through this process is not as reputable and may be flawed.